
 # A dictionary of movie critics and their ratings of a small
# set of movies
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
 'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5, 
 'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5, 
 'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0, 
 'You, Me and Dupree': 3.5}, 
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
 'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
 'The Night Listener': 4.5, 'Superman Returns': 4.0, 
 'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 
 'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
 'You, Me and Dupree': 2.0}, 
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
 'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}



from math import sqrt

def sim_distance(prefs, person1,person2):
    #Get the list of shared items
    si = {}

    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1;

    # Return 0 if there is nothing similar
    if len(si) == 0:
        return 0

    # Add the sum of squares
    sum_of_squares = sum( [pow(prefs[person1][item] - prefs[person2][item], 2)
                           for item in prefs[person1] if item in prefs[person2]])


    # Since we need a score that will be higher = BETTER results, we will give the inverse (+1 to avoid divide by zero)
    return 1/(sqrt(sum_of_squares) + 1)



def sim_pearson(prefs, person1, person2):

    # Get the list of things that are similar
    si = {}

    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1;

    if len(si) == 0: return 0

    n = len(si)

    # Find the sum 
    sum_of_person1 = sum([ prefs[person1][item]
                           for item in si])

    sum_of_person2 = sum([ prefs[person2][item]
                           for item in si])

    sum_of_product = sum([ prefs[person1][item] * prefs[person2][item]
                           for item in si])

    sum_of_person1_square = sum([ pow(prefs[person1][item],2)
                                  for item in si])

    sum_of_person2_square =  sum([ pow(prefs[person2][item],2)
                                  for item in si])


    num = sum_of_product - (sum_of_person1*sum_of_person2)/n
    dem = sqrt( (sum_of_person1_square - pow(sum_of_person1,2)/n) *
                (sum_of_person2_square - pow(sum_of_person2,2)/n))

    if dem == 0 : return 0

    return num/dem

                

    
def top_matches( data, person, n = 5, sim_measure = sim_distance ):

    scores = [ (sim_measure(data, person, other),other)
               for other in data if other != person]

    scores.sort()
    scores.reverse()

    return scores[0:5]

    
    

